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OUTCOME IN RECIPIENTS OF DUAL KIDNEY TRANSPLANTS

2000· article· en· W2413361059 on OpenAlexaff
Amy D. Lu, Jonathan Carter, Rebecca Weinstein, Robert J. Stratta, Rodney J. Taylor, Victor Bowers, Lloyd E. Ratner, Kenneth D. Chavin, Lynt B. Johnson, Paul C. Kuo, Edward Cole, Donald C. Dafoe, Edward J. Alfrey

Bibliographic record

VenueTransplantation · 2000
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineKidneyIncidence (geometry)Single CenterKidney transplantationRetrospective cohort studyKidney transplantTransplantationSurgeryRenal functionInternal medicineUrology

Abstract

fetched live from OpenAlex

BACKGROUND: A novel but controversial method to increase the utilization of aged donor kidneys is the transplantation of both kidneys as a dual transplant. Initial single-center reports demonstrated outcomes similar to single kidneys from younger donors. In this report, we compare outcome in recipients of kidneys from donors > or =54 years of age who received a single kidney transplant reported to the United Network for Organ Sharing Scientific Registry versus a dual kidney transplant reported to the Dual Kidney Registry. METHODS: A retrospective analysis was performed, comparing four donor and nine recipient and outcome variables between recipients of a single versus a dual transplant between March 1993 and March 1999. RESULTS: Dual versus single transplants from donors > or =54 years of age have a significantly decreased incidence of delayed graft function, and lower serum creatinines up to 2 years after transplant despite having kidneys from significantly older donors with poorer HLA matching. CONCLUSIONS: Dual kidney transplants improve graft performance and outcome in recipients of kidneys from donors > or =54 years of age.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.315
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations59
Published2000
Admission routes1
Has abstractyes

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